2026-07-14 –, Intro
Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets. Many real-world datasets fit this structure. However, a common roadblock for users is knowing how to load the data in Xarray and then how to best use Xarray’s tools to represent the structure of the data. In this hands-on tutorial we will showcase how to work with Xarray, various ways to get real-world data into Xarray (with examples from geosciences and biology) and finally how to easily make complex selections on data using community developed custom indexes.
Installation Instructions: https://tutorial.xarray.dev/workshops/scipy2026/index.html
In this hands-on tutorial, users will work with example data from multiple fields of science (including biology and geosciences) to achieve these learning objectives:
Understand xarray’s core data structures
- Named arrays and coordinates (
Variable) - Groups of arrays with coordinates (
DataArrayandDataset) - Hierarchical trees of related groups (
DataTree)
Understand how to load data from different formats as an Xarray object with different access patterns:
- VirtualiZarr
- Intake
- Backend engines
- Rioxarray (rasterio)
- pyDAP
- Zarr
- Icechunk
How to use Xarray flexible indexes to make queries on the data once it is loaded
- Forecasts
- Tree based indexing
- Lazy Out of Memory
Familiarity
This hands-on tutorial assumes participants have some familiarity with Jupyter Notebooks, NumPy, Pandas, and Xarray, and focuses on intermediate workflows using real-world datasets. All material will be presented in curated Jupyter Notebooks with exercises to solidify understanding of key concepts. Tutorial material is available online with instructions for running examples on free hosted infrastructure or on a local computer. No specific scientific domain expertise is required to participate effectively in this tutorial. Example datasets will either be small enough to download locally or available as in public cloud buckets.
We encourage participants to review last year’s tutorial prior to attending and bring your questions and enthusiasm to make our 4-hour session as interactive as possible!
I am working as an Xarray community developer at Earthmover. In this role I am focused on improving Xarray’s support and documentation for the biology/biomedical community. Prior to this I completed my PhD in which I extensively used Xarray, zarr and the Pydata stack to implement custom microscope control software and analyze multimodal timelapse single cell microscopy data. I loved the open source scientific software so much that now I get to work full time improving it and sharing it with others.
Nick Hodgskin is a Research Software Engineer and Xarray maintainer working at Utrecht University, primarily on Parcels - a Lagrangian simulation framework used in physical oceanography. Here he has been leading a rewrite of Parcels to use Xarray as a core data structure, along the way improving Parcels interoperability with the Pangeo ecosystem of packages. A self proclaimed "Pangeo evangelist", Nick loves communicating the power of the Scientific Python stack with Xarray for geospatial analysis - which he does by organising fortnightly talks at his institute, as well as by giving tutorials. When he isn't coding, you can find Nick playing Ultimate frisbee, hiking in nature, reading, or learning languages.
Eni is a scientific software developer at NASA Goddard’s Earth Science and Information Services Center (GESDISC) and Xarray core developer. At GESDISC she uses open-source tools to create services in support of NASA’s vast earth science catalog and contributes to several enterprise NASA ESDIS tools. She is interested in using computational science to improve our understanding of the natural world.